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Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference

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  • Nikolaus Hautsch
  • Yangguoyi Ou

Abstract

In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2008-063.pdf
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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-063.

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Length: 25 pages
Date of creation: Sep 2008
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Handle: RePEc:hum:wpaper:sfb649dp2008-063

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Related research

Keywords: Stochastic Volatility; Markov Chain Monte Carlo; Metropolis-Hastings al- Jump Processes;

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